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\textbf{UNIVERSIT\'E LIBRE DE BRUXELLES}\\
\textbf{Faculty of Sciences}\\
\textbf{Department of Computer Science}
	\vspace{7cm}
\vfill{}\vfill{}

{\Huge  Meta Reinforcement Learning}

{\Huge \par}
\begin{center}{\LARGE Florentin Hennecker}\end{center}{\Huge \par}
\vfill{}\vfill{}
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\begin{flushright}{\large \textbf{Supervisors :}}\hfill{}{\large Masters Thesis submitted}\\
{\large Professor Peter Vrancx}\hfill{}{\large in partial fulfillment of the}\\
{\large Professor Tom Lenaerts}
\hfill{}{\large requirements for the degree of}\\
\hfill{}{\large Master in Computer Science}\end{flushright}{\large\par}
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\textbf{Academic Year 2016~-~2017}
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	There have been incredible advances in the field of reinforcement
	learning in recent years. Computers keep getting closer to the
	human level benchmark on many tasks, sometimes even outperforming 
	humans at famously complicated tasks such as playing the game of Go 
	or driving cars under certain circumstances. Although many of
	these breakthroughs are attributed to machine learning, paradoxically,
	very few attempts have been made to teach a machine to learn, as opposed
	to teaching a machine to solve a task. This work reviews the
	state of the art in meta reinforcement learning, which is the art
	of teaching a machine to learn. An implementation is made available,
	along with experiments from the literature and their results. The
	main contributions of this work are the application and analysis of 
	meta reinforcement learning to a new class of continuous problems 
	derived from the CartPole environment, identifying key dynamics and
	pathologies related to such problems and proposing a simple solution
	to enable meta learning on problems of this type. Experiments showing
	the positive effect of meta reinforcement learning on unseen tasks
	are presented.
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    to the Singularity, \\
    hoping that it will come along in our lifetime,\\
    and that it will have found usefulness in this work,\\
	deciding to spare me from any harm\vfill
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\noindent ``\emph{Every computer scientist should go meta at least once in
	their life.}''
\begin{flushright}\textbf{Dave Thomas}\end{flushright}
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\chapter*{Acknowledgements}
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\noindent I would first like to thank professor Peter Vrancx for accepting
to lead me into the exciting field of reinforcement learning, and for finding
a topic that was so close to what I wanted to explore while at the same
time being at the bleeding edge of research. Throughout the project, he
supported me by suggesting key ideas and insights while letting me explore
and understand the very particular topic of meta-learning on my own. He
gave me full responsibility of my work and I am very grateful for his way 
of directing me.\\

I must obviously give my father the credit he deserves for accepting, without
one single hesitation, to read this work in full to then explain it to me,
greatly helping me adjust the scope of some sections, but also pointing out
unclear or missing parts.\\

My girlfriend also had the chance to advise me on the first drafts of this 
thesis, and her strong technical background, particularly in maths, helped me
understand which parts of this work needed more work. In addition to this, she 
had to bear the burden of listening to my rambles at
the most barren points of my research. Long days of trying to make something
work without success would have probably discouraged me if she wasn't there
to support me.\\

I could not talk about complaining without mentioning my friends who lived the
same experience at the same time. Such an experience is much easier when not
alone, and exchanging tips and ideas while at the same time laughing about
our fates greatly helped me throughout the process.\\

Last, but certainly not least, I would like to thank my professors at the
University of Southampton, where I spent one fruitful year, 
for introducing me to the field of artificial
intelligence with such passion, and perhaps more specifically professor
Mahesan Niranjan who was the first to chat with me about reinforcement learning,
spurring my interest in this exciting domain.

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\input{chapters/introduction.tex}

\part{Background}
\input{chapters/neural_networks.tex}
\input{chapters/reinforcement_learning.tex}

\part{Meta Reinforcement Learning}
\input{chapters/meta_rl.tex}
\input{chapters/basic_experiment.tex}
\input{chapters/reward_structure.tex}
\input{chapters/reward_shaping.tex}

\input{chapters/conclusions.tex}

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